An improved DV-Hop algorithm based on PSO and Modified DE algorithm

Wireless sensor networks (WSN) have been used in many fields, and the localization technology is one of the core technologies of WSN. Distance Vector-Hop (DV-Hop) algorithm is one of the localization algorithms for WSN, which is widely used because of its simple principle and low cost. The traditional DV-Hop algorithm has high localization error, so the PMDDV-Hop algorithm is proposed in this paper. First, the average hop-size of anchor nodes is optimized by the Particle Swarm Optimization (PSO) algorithm to reduce the accumulation of errors. Then the coordinates of the unknown nodes are optimized using the Differential Evolutionary (DE) algorithm. To reduce the probability of falling into local optimum during evolution, the levy flight strategy is introduced into the DE algorithm to increase the diversity of the population. To further improve the performance of the PMDDV-Hop algorithm, the mutation factor and crossover factor in the DE algorithm are dynamically changed to make them adaptive to the degree of population evolution. Finally, extensive experimental simulations are conducted to evaluate the effectiveness of the PMDDV-Hop algorithm. Experimental results show that the PMDDV-Hop algorithm can effectively reduce the localization error.